Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. It provides better predictive ability than principle component analysis by taking into account both the independent and response variables in the dimension reduction procedure. However, PLS suffers from over-fitting problems for few samples but many variables. We formulate a ne...

In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to flexible neural network architecture, our methodology uses dimension reduction and feature selection from set instruments covariates. A central theoretical result, due Brillinger (2012) Selected Works Daving Brillinger. 589-606, shows that pro...

Entrepreneurial orientation has recently been touted as a tool for solving enterprise failures in emerged and emerging economies especially during after an epidemic. This study aims at understanding the impact of entrepreneurial on performance Ghanaian food processing industry by assessing mediating effects innovation types intellectual property. Data were collected from 702 owners/managers via...

This research is intended to determine the impact of transformation MSME tax rates as well modernization on administration system complying taxpayer with knowledge which usually in form moderation variations. implements a quantitative approach and data collection techniques by distributing questionnaires number samples that are 64 respondents convenience sampling method. Partial least squares (...

−The accurate and reliable on-line estimation of product quality is an essential task for successful process operation and control. This paper proposes a new estimation method that extends the conventional linear PLS (Partial Least Squares) regression method to a nonlinear framework in a more robust manner. To handle the nonlinearities, nonlinear PLS based on linear PLS and neural network has b...

Early detection of cancer is crucial for successful treatments. In this paper, we propose a multiclass Logistic Partial Least Squares (LPLS) algorithm for classification of normal vs. cancer using Mass Spectrometry (MS). LPLS combines the multiclass logistic regression with Partial Least Squares (PLS) algorithm. Wavelet decomposition is also proposed for pre-processing of original data. Wavelet...

Quantitative structure-property relationship (QSPR) analysis has been directed to a series of pure nonionic surfactants containing linear alkyl, cyclic alkyl, and alkey phenyl ethoxylates. Modeling of cloud point of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. In this study,...

Hybrid linear analysis (HLA), partial least-squares (PLS) regression, and the linear least square support vector machine (LSSVM) were used to determinate the soluble solids content (SSC) of apple by Fourier transform near-infrared (FT-NIR) spectroscopy. The performance of these three linear regression methods was compared. Results showed that HLA could be used for the analysis of complex solid ...

Visible and near-infrared hyperspectral imaging covering spectral range of 380-1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500-900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS...